1. Field of the Invention
The present invention relates to an image recognition apparatus, an image recognition method, and a program.
2. Description of the Related Art
Conventionally, a face recognition technique for identifying a person such that a face area (i.e., face image) is extracted from an image including a person's face to compare thus extracted face image with a face image of a specific person who is preliminary registered is known.
The above technique is used for a security purpose, e.g., for allowing a person appearing in camera to enter into an office when the person appearing in camera is verified as a registrant. On the other hand, there is a demand for using the technique for searching a picture in which the same person appears.
In the former use, limiting conditions imposed on taking a picture of a person enable a highly accurate recognition of the person. In the latter use, however, there is such a problem that degradation of recognition accuracy of the person occurs since shooting conditions of the person are wide ranging. For example, such a false recognition may occur that the person may be recognized as a different person even if the same person is in the pictures when the pictures are taken with a different face direction, a different facial expression, and different lighting upon taking each picture.
To solve the above problem, a recognition method in which a plurality of local areas is extracted from a face image to recognize the person based on similarities of the local areas is proposed. For example, a method in which the verification is made against each local area of the face image by the principal component analysis to improve a robustness of a face direction and a hidden face is discussed (refer to Pentland, Moghaddam and Starner. View-based and modular eigenspaces for face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), which hereinafter will be referred to as “Literature 1”). The local areas here are parts representing characteristic areas of a face such as eyes, a nose, and a mouth.
In addition, not merely based on the similarity of an image, a method in which the recognition accuracy is remarkably improved by personal identification of a face based on an attribute and a similarity with a representative person is discussed (refer to Kumar, Berg, Belhumeur and Nayar. Attribute and Simile Classifiers for Face Verification. IEEE 12th International Conference on Computer Vision (ICCV2009), which hereinafter will be referred to as “Literature 2”). The attribute here is exemplified by “big, round eyes” and “slender eyes”, i.e., types of a shape of the eyes, in a case of the eyes. The similarity with the representative person here is exemplified by “similar to the eyes of Mr. A” and “similar to the eyes of Mr. B”. In the following description, the attribute includes the similarity with the representative person.
In other words, in Literature 2, the recognition is performed based on a determination that when a person of an input face image and a person of a registered face image, i.e., a registrant's face image, have the same attribute, both persons are the same person.
However, in the method of Literature 2, identification processing is performed by a support vector machine using an Radial Basis Function (RBF) kernel in order to acquire a degree with respect to a certain attribute of the local area. The identification processing is performed with a plurality of feature quantities selected by previous learning.
This is because the method of Literature 2 requires a high accuracy in identifying the attribute. As a result thereof, the identification processing for identifying the attribute has complexity. It is difficult to be hard-wired because of the complexity of the identification processing.